Can the soil seed bank of Rumex obtusifolius in productive grasslands be explained by management and soil properties?

Rumex obtusifolius is a problematic weed in temperate grasslands worldwide as it decreases yield and nutritional value of forage. Because the species can recruit from the seed bank, we determined the effect of management and soil properties on the soil seed bank of R. obtusifolius in intensively managed, permanent grasslands in Switzerland (CH), Slovenia (SI), and United Kingdom (UK). Following a paired case-control design, soil cores were taken from the topsoil of grassland with a high density of R. obtusifolius plants (cases) and from nearby parcels with very low R. obtusifolius density (controls). Data on grassland management, soil nutrients, pH, soil texture, and density of R. obtusifolius plants were also collected. Seeds in the soil were germinated under optimal conditions in a glasshouse. The number of germinated seeds of R. obtusifolius in case parcels was 866 ±152 m-2 (CH, mean ±SE), 628 ±183 m-2 (SI), and 752 ±183 m-2 (UK), with no significant difference among countries. Densities in individual case parcels ranged from 0 up to approximately 3000 seeds m-2 (each country). Control parcels had significantly fewer seeds, with a mean of 51 ±18, 75 ±52, and 98 ±52 seeds m-2 in CH, SI, and UK, respectively, and a range between 0 and up to 1000 seeds m-2. Across countries, variables explaining variation in the soil seed bank of R. obtusifolius in case parcels were soil pH (negative relation), silt content (negative), land-use intensity (negative), and aboveground R. obtusifolius plant density (positive). Because a large soil seed bank can sustain grassland infestation with R. obtusifolius, management strategies to control the species should target the reduction in the density of mature plants, prevention of the species’ seed production and dispersal, as well as the regulation of the soil pH to a range optimal for forage production.

1 S1 Appendix. Supporting information on germination conditions, analyses of soil nutrients and data analysis Land-use intensity Information received from the farmers about the duration of grazing periods and the number of livestock allowed to calculated the grazing intensity as livestock unit days of grazing ha −1 year −1 . The amount of plant-available nitrogen (N) applied per year was calculated for the different forms of mineral and organic fertilisers, following standard tables of the three countries (Richner et al., 2017;Mihelič et al., 2010;AHDB, 2021). Data on plant-available N applied, mowing, and grazing intensity was then used to calculate the quantitative, continuous index of land-use intensity (LUI) following Blüthgen et al. (2012). Because we had to assume differing management intensities among the three countries due to differing pedo-climatic conditions, the LUI index was standardised following Blüthgen et al. (2012) with the respective country means of the three components of the index: fertilisation (plant-available nitrogen applied), mowing (number of mowing events), and grazing intensity (livestock unit days of grazing ha −1 year −1 ). As suggested by Blüthgen et al. (2012), we applied the square-root transformation on the LUI to achieve a more even distribution. These values are presented in S2 Table. Germination conditions The germination test for CH and SI samples took place in a shaded glasshouse of Agroscope, Zürich, that had light (natural and supplementary) between 06:00 and 22:00 (16h/8h diurnal cycles). Temperature were generally maintained at 24 °C during daytime and at 18 °C during night, but temperatures sometimes rose to 31 °C during warm days. These conditions were within the optimal conditions for the germination of seeds of R. obtusifolius (Totterdell & Roberts, 1980). The seed bank samples from the UK were germinated in a glasshouse at Rothamsted Research in the months of March, April and May, set up to replicate the conditions at Agroscope except that supplementary lighting was not used because of high natural light levels. Average daytime temperatures (06:00-22:00) were 25.5 °C and night time temperatures 18.9 °C. The experimental set-up was the same as described in the main text. Due to the pandemic situation, 2 access to the experiment was allowed for only 3 times over the planned time of 63 days and the soil substrate was crumbled only once. The experiment, however, was run for another 14 days.

Analysis of soil nutrients and soil texture
Soil samples were first re-dried to 40 °C and sieved through a 2 mm mesh before analysis.
Concentrations of phosphorus (P), potassium (K), magnesium (Mg), and calcium (Ca) were determined using extractions with ammonium acetate and ethylene-diamine-tetraacetic acid (EDTA). 10 g soil was extracted for 1 h in 100 mL of a solution of 0.5 M ammonium acetate, 0.5 M acetic acid and 0.02 M EDTA at pH 4.65 (temperature 23 ± 1 °C). After 1 h paper filtration, K, Mg and Ca concentration are determined by ICP-OES (inductively coupled plasma-optical emission spectroscopy).
The pH was determined by mixing one part of soil with 2.5 parts of distilled water. The mixture was equilibrated between 12 and 18 hours before measurement.
Organic carbon (C-org) was determined with potassium dichromate in sulphuric acid, which oxidizes the organic carbon to CO2. Back-titration of the remaining dichromate with Fe 2+ allows the calculation of C-org (see Agroscope, 2020 for details). Percentage values of clay and silt were determined by sedimentation, while percentage sand was calculated as the difference between the sum of the first three parameters (C-org × 1.725, clay, silt) and 100%.

Data analysis
The number of germinated seeds of R. obtusifolius at the end of the germination experiment Pr(Si = 0) = πi logit(π) = γ1 + β1Par_Type 3 with parcel type (Par_Type) being a factor of two levels (0 for control parcels, 1 for case parcels). The analysis revealed that only the variable Parc_Type had a relevant effect on π, but not country.
If Si > 0: S ~ Truncated NB(μ,α), with α being a scaling parameter E(S) = (1 − π) μ 1 − (1 + μ) / log(μ) = γ2 + β2Par_Typei + β3Countryi + β4Parc_Type×Country + λTray where 'Country' is a factor with three levels (CH, SI, UK) and λ is a random parameter with λ ~ N(0, σ 2 ). The λ models the effect of the metal trays, each containing the control and case sample from one site (see Fig 1, main text). A joint maximum likelihood is calculated for both component models to estimate the fixed parameters and the random variance. Residuals were evaluated and met the assumptions of the applied model.